The live streaming of child sexual abuse (CSA) is a technologically and financially enabled crime type which has proliferated in recent years. This study uses a machine learning approach to produce a proof of concept model for identifying the financial indicators associated with CSA live streaming.
This model was successful at identifying those who live streamed child sexual abuse, while making few errors in identifying those who did not.
Seven financial risk indicators were identified. Six risk indicators centred on the value of transactions, and one on the age of the individual making the transactions. These findings reveal an important opportunity to use financial transactions as an avenue for detecting and disrupting CSA live streaming.
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URLs correct as at March 2025
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